When
Where
Available in person and via zoom (see email for link)
Abstract
The increasing severity of hydrometeorological extremes, driven by climate change, poses significant risks to lives and infrastructure globally. This presentation examines, from both technical and broad perspectives, research efforts focused on short-term forecasts, long-term climate projections, and streamflow simulations of extremes in arid regions. Achieving these research objectives require management of large datasets and the development of efficient, reproducible computational processes.
The University of Arizona’s Roots for Resilience (R4R) program is a specialized training cohort that prepares graduate students with expertise in open, reproducible science, computational infrastructure, and AI tools across disciplines. This talk features the computational framework developed for research on hydrometeorological extremes and explores how R4R training informs and strengthens its design. By using examples of real-world applications across various projects, from short-term forecasts to long-term climate projections, this work illustrates the integration of some of these tools into data management, model execution, and results publication. It also highlights how the R4R toolkit can be retroactively applied to existing research, guide current projects, and enhance future work to ensure reproducible and impactful science.
Bio
M. del R. Lourdes Mendoza Fierro is a Ph.D. candidate at the UA Department of Hydrology and Atmospheric Science. She obtained her B.S. in Geophysical Engineering from the National Autonomous University of Mexico (UNAM) and M.S. in Hydrometeorology from the UA. Lourdes is part of Dr. Hsin-I Chang and Dr. Chris Castro's research group and is currently part of the Roots for Resilience (R4R) 2025 Cohort. Her research explores weather models and climate data across timescales, using short-term forecasts, seasonal outlooks, and long-term climate projections to understand hydrometeorological extremes and risks.
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